Recommendation systems power engagement and monetization across feeds, ads, and short-video platforms, but translating the latest advances in Large Language Models into Recommendation Systems (RecSys) gains remains rare, particularly in advertising and production-scale real-world industry setups. Prior real-world LLM successes typically fall into three buckets: (a) generative retrieval that directly predicts the next items for candidate generation, (b) late-stage re-ranking that uses LLMs, and (c) auxiliary signal enrichment with LLMs. We introduce a complementary paradigm for ads: a fine-tuned open-source LLM used not as a ranker, but as an ads-specific ancillary predictor, forecasting likely advertisers from user profiles and histories. This LLM-driven advertiser prediction augments conventional candidate generation and provides informative priors to downstream ranking. Developed in a large-scale production advertising system, our approach produces substantial offline improvements and measurable online business impact, demonstrating that LLM world knowledge and predictive capacity can be efficiently harnessed. Beyond validating LLMs for ads applications, our results show that targeted ancillary predictions can unlock end-to-end gains across both retrieval and late-stage ranking, offering a practical path to LLM-enhanced recommendation at scale.

翻译：推荐系统驱动着信息流、广告和短视频平台的用户参与和商业变现，但将大语言模型的最新进展引入推荐系统（RecSys）的情况仍较为罕见，特别是在广告系统和工业级实际部署场景中。先前大语言模型在真实场景中的成功应用主要归为三类：（a）直接预测下一候选项目的生成式检索；（b）利用大语言模型进行的后期重排序；（c）利用大语言模型增强辅助信号。我们提出一种面向广告的互补范式：将经过微调的开源大语言模型并非用作排序器，而是作为广告专用的辅助预测器，从用户画像和历史行为中预测潜在广告商。这种基于大语言模型的广告商预测增强了传统候选生成流程，并为下游排序提供信息性先验知识。在大型生产级广告系统中开发的方法，带来了显著的离线指标提升和可衡量的在线业务影响，证明大语言模型的世界知识和预测能力可被高效利用。除了验证大语言模型在广告场景的应用价值外，我们的研究结果表明，针对性的辅助预测能够同时解锁检索和后期排序阶段的全链路增益，为大规模大语言模型增强型推荐提供了实用路径。